High performance data compression systems use models of the data to increase their ability to predict values, which in turn leads to greater compression. The best models can be achieved by building a compression system to support a specific data format. Instead of trying to deduce a crude model from the data within a specific file, a format-specific compression system can provide a precise pre-determined model. The model can take advantage not just of the file format structure, but also of statistical data gathered from sample databases.
Previous efforts at format-specific compression have been focused on solutions to a few individual formats rather than on the development of a generalized method that could be adapted to many formats. The models which have been created typically involve a small number of components. This works adequately when most of the data is included in a few components, such as an image file having mostly red, blue, and green pixel values. But may formats are best modeled using a large number of components, and the previous systems are not designed to build or encode such models.